Neural Related Work Summarization with a Joint Context-driven Attention Mechanism (D18-1)
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| Challenge: | Existing approaches to automatic related work summarization rely on human-engineered features. |
| Approach: | They propose a neural data-driven attention mechanism to measure contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. |
| Outcome: | The proposed approach achieves significant improvement over a typical seq2seq summarization baseline and five classical summarizing baselines. |
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| Challenge: | Existing summarization systems rely on the source text to generate summaries, which tends to work unstably. |
| Approach: | They propose to use existing summaries as soft templates to guide the seq2seq model . they use a popular IR platform to Retrieve proper summary as candidate templates . |
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Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context . |
| Approach: | They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion. |
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Dynamic Structured Neural Topic Model with Self-Attention Mechanism (2023.findings-acl)
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| Challenge: | Recent topic models that capture the time-series evolution of topics assume that topics evolve independently without interaction. |
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Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)
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| Challenge: | Existing methods for extractive and abstractive summarization are far from human performance. |
| Approach: | They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context. |
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Focus Attention: Promoting Faithfulness and Diversity in Summarization (2021.acl-long)
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| Challenge: | Currently, document summarization is challenging even for humans. |
| Approach: | They propose a focus attention mechanism which encourages decoders to generate tokens that are topically similar to the input document. |
| Outcome: | The proposed method outperforms top-k and nucleus sampling methods on the BBC extreme summarization task and is more accurate than focus attention-based models. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
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Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation (2023.emnlp-main)
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Yixin Liu, Alexander Fabbri, Yilun Zhao, Pengfei Liu, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
| Challenge: | Compared to neural systems, automatic metrics should be interpretable and provide intuitive insights into system performance and output quality. |
| Approach: | They propose to use a two-stage evaluation pipeline to extract basic information units from one text sequence and check the extracted units in another sequence. |
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Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization (P18-2)
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| Challenge: | Existing abstractive text summarization models learn a semantic representation of the source text and the summaries from it. |
| Approach: | They evaluate the model on a popular Chinese social media dataset and compare it to other models. |
| Outcome: | The proposed model achieves state-of-the-art performance on a popular Chinese social media dataset. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
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| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
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SummHelper: Collaborative Human-Computer Summarization (2023.emnlp-demo)
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| Challenge: | Existing approaches for text summarization are mostly automated, with limited space for human intervention and control. |
| Approach: | They propose a 2-phase summarization assistant that facilitates human-machine collaboration . it suggests possible content and generates a coherent summary from these selections . authors hope to improve the efficiency of the computer and human-involved approach . |
| Outcome: | The proposed summarization assistant is a 2-phase summarizing assistant . it suggests potential content and consolidates the output with visual mappings . the proposed system is available for free on youtube . |